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Deep Learning ECG Segmentation for Diverse Arrhythmias

Mem0: Scalable Long-Term Memory for AI Agents

Prateek Chhikara, Dev Khant +3

Large Language Models (LLMs) have demonstrated remarkable prowess in generating contextually coherent responses, yet their fixed context windows pose fundamental challenges for…

SkillOpt — Executive Strategy for Self-Evolving Agent Skills

Yifan Yang, Ziyang Gong +13

Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill,…

RGSD: Rubric-Guided Self-Distillation

MohammadHossein Rezaei, Anas Mahmoud +7

Rubrics have emerged as an alternative to RLVR in open-ended domains where a single ground-truth final answer is not available. Existing rubric-based training methods rely on an…

Reflexion: Language Agents with Verbal Reinforcement Learning

Noah Shinn, Federico Cassano +4

Large language models (LLMs) have been increasingly used to interact with external environments (e.g., games, compilers, APIs) as goal-driven agents. However, it remains…

Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillation

Shi-Yu Tian, Yong Liu +11

Multi-teacher on-policy distillation (MOPD) extends on-policy distillation (OPD) by routing domain-specific data to specialized teachers, integrating their capabilities through…

LatentLoop: Scaling Looped MoE Models with Asymmetric Compute Allocation

Zefeng He, Yunkai Chai +6

Looped Mixture-of-Experts (MoE) models combine parameter reuse with sparse computation to improve performance under matched budgets. Prior work demonstrates that different loop…

Cortex: Consolidating Context into Parametric Memory — Research Breakdown

Jintian Zhang, Feng He +6

Context can improve large language model performance, but these gains may be lost after context removal. Inspired by biological systems consolidation, we formulate Memory…

RWKV: Reinventing RNNs for the Transformer Era

Bo Peng, Eric Alcaide +32

Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence…

Model Domain Mapping Function & Composition

Space Object Detection via Multi-frame Temporal Trajectory Completion

The Empowerment of Science of Science by LLMs

Tülu 3: Pushing Frontiers in Open Language Model Post-Training

Nathan Lambert, Jacob Morrison +21

Language model post-training is applied to refine behaviors and unlock new skills across a wide range of recent language models, but open recipes for applying these techniques…

Kimi k1.5: Scaling Reinforcement Learning with LLMs

Kimi Team, Angang Du +94

Language model pretraining with next token prediction has proved effective for scaling compute but is limited to the amount of available training data. Scaling reinforcement…

The Entropy Mechanism of RL for Reasoning LLMs

Ganqu Cui, Yuchen Zhang +15

This paper aims to overcome a major obstacle in scaling RL for reasoning with LLMs, namely the collapse of policy entropy. Such phenomenon is consistently observed across vast RL…

1-Shot RLVR: Reinforcement Learning for Reasoning with One Example

Yiping Wang, Qing Yang +12

We show that reinforcement learning with verifiable reward using one training example (1-shot RLVR) is effective in incentivizing the math reasoning capabilities of large…

Spurious Rewards: Rethinking Training Signals in RLVR

Rulin Shao, Shuyue Stella Li +12

We show that reinforcement learning with verifiable rewards (RLVR) can elicit strong mathematical reasoning in certain language models even with spurious rewards that have…

Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Andrew Zhao, Yiran Wu +9

Reinforcement learning with verifiable rewards (RLVR) has shown promise in enhancing the reasoning capabilities of large language models by learning directly from outcome-based…

Does RL Really Expand LLM Reasoning?

Yang Yue, Zhiqi Chen +6

Reinforcement Learning with Verifiable Rewards (RLVR) has recently demonstrated notable success in enhancing the reasoning performance of large language models (LLMs),…

Understanding R1-Zero-Like Training: A Critical Perspective

Zichen Liu, Changyu Chen +6

DeepSeek-R1-Zero has shown that reinforcement learning (RL) at scale can directly enhance the reasoning capabilities of LLMs without supervised fine-tuning. In this work, we…

DAPO: Demystifying Large-Scale LLM Reinforcement Learning

Qiying Yu, Zheng Zhang +33

Inference scaling empowers LLMs with unprecedented reasoning ability, with reinforcement learning as the core technique to elicit complex reasoning. However, key technical…

s1: Simple Test-Time Scaling

Niklas Muennighoff, Zitong Yang +8

Test-time scaling is a promising new approach to language modeling that uses extra test-time compute to improve performance. Recently, OpenAI's o1 model showed this capability…

DeepSeekMath: Pushing the Limits of Mathematical Reasoning

Zhihong Shao, Peiyi Wang +9

Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature. In this paper, we introduce DeepSeekMath 7B, which continues…

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL

DeepSeek-AI, Daya Guo +198

General reasoning represents a long-standing and formidable challenge in artificial intelligence. Recent breakthroughs, exemplified by large language models (LLMs) and…